Requirements Development and Formalization for Reliable Code Generation: A Multi-Agent Vision
On the map: LLM agents and tool use → Software engineering coding agents
Abstract (arXiv)
Automated code generation has long been considered the holy grail of software engineering. The emergence of Large Language Models (LLMs) has catalyzed a revolutionary breakthrough in this area. However, existing methods that only rely on LLMs remain inadequate in the quality of generated code, offering no guarantees of satisfying practical requirements. They lack a systematic strategy for requirements development and modeling. Recently, LLM-based agents typically possess powerful abilities and play an essential role in facilitating the alignment of LLM outputs with user requirements. In this paper, we envision the first multi-agent framework for reliable code generation based on textscrequirements textscdevelopment and textscformalization, named textscReDeFo. This framework incorporates three agents, highlighting their augmentation with knowledge and techniques of formal methods, into the requirements-to-code generation pipeline to strengthen quality assurance. The core of textscReDeFo is the use of formal specifications to bridge the gap between potentially ambiguous natural language requirements and precise executable code. textscReDeFo enables rigorous reasoning about correctness, uncovering hidden bugs, and enforcing critical properties throughout the development process. In general, our framework aims to take a promising step toward realizing the long-standing vision of reliable, auto-generated software.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 19 of its 48 references that are in the map. First posted on arXiv 26 Aug 2025.
Builds on
19 of 48 references.
Closest ancestors · 3
Same direction, newest first
- 2025Knowledge-Based Multi-Agent Framework for Automated Software Architecture Design26 Mar
- 2024Elicitron: An LLM Agent-Based Simulation Framework for Design Requirements Elicitation4 Apr
- 2023ChatDev: Communicative Agents for Software Development16 Jul
Other references · 16
- 2023GPT-4 Technical Reportcited by 18,396
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
- 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
- 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
- 2024A Survey on Large Language Models for Code Generationcited by 658
- 2024DeepSeek LLM: Scaling Open-Source Language Models with Longtermismcited by 642
- 2022Autoformalization with Large Language Modelscited by 263
- 2024Enchanting Program Specification Synthesis by Large Language Models using Static Analysis and Program Verificationcited by 58
- 2024SpecGen: Automated Generation of Formal Program Specifications via Large Language Modelscited by 53
- 2022Formal Specifications from Natural Languagecited by 33
4 more
- 2024Source Code Summarization in the Era of Large Language Modelscited by 31
- 2024Requirements are All You Need: From Requirements to Code with LLMscited by 22
- 2025From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofscited by 18
- 2024Combining LLM Code Generation with Formal Specifications and Reactive Program Synthesiscited by 7
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2026Code as Agent Harnesscited by 47
- 2025Stop Wasting Your Tokens: Towards Efficient Runtime Multi-Agent Systemscited by 12
- 2025AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolutioncited by 0
- 2026Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Developmentcited by 0
- 2026Don't Guess, Just Ask: Evaluating Interactive Requirement Clarification for Repository-Level Software Development9 Oct 2026
- 2026Code as Agent Harness18 May 2026
- 2025AutoICE: Automatically Synthesizing Verifiable C Code via LLM-driven Evolution8 Dec 2025
- 2025Stop Wasting Your Tokens: Towards Efficient Runtime Multi-Agent Systems30 Oct 2025
Similar, not cited
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- 2025A Survey on Code Generation with LLM-based Agents
- 2026On the Viability of Requirements Generation From Code: An Experience Report ↗
- 2025CodeCoR: An LLM-Based Self-Reflective Multi-Agent Framework for Code Generation
- 2026Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking